Unlike traditional multimedia content, content generated on social media platforms such as YouTube, Flickr etc are usually annotated with rich set of social tags such as keywords, textual description, category information, author's profile etc. In this paper we investigate the use of such social tag information for visual diversification of image search results in Flickr. We model search result diversity as an instance of the p-dispersion problem where the objective is to choose p out of n given points, so that the minimum distance between any pair of chosen points is maximized. The distance metric used in the p-dispersion problem is learnt from the data itself by combining candidate similarity measures as defined on the social tags. We demonstrate the effectiveness of our proposed method on a real-world data set.